add boxes

app.py

@@ -28,17 +28,12 @@ model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
 with open("prompt.txt", "r") as file:
     system_prompt = file.read()
 
-st.title("Real-World Programming Quesion Mock Interview")
+st.title("Real-World Programming Question Mock Interview")
 
-# Initialize chat history
+# Initialize chat history in session state
 if "messages" not in st.session_state:
     st.session_state.messages = [{"role": "assistant", "content": system_prompt}]
 
-# Display chat messages from history on app rerun
-for message in st.session_state.messages[1:]:  # Skip the system message
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
 # Function to find the top 1 most similar question based on user input
 def find_top_question(query):
     # Generate embedding for the query
@@ -70,15 +65,21 @@ def generate_response(prompt):
     )
     return response.choices[0].message.content
 
-# React to user input
-if prompt := st.chat_input("Enter a LeetCode-related query (e.g., 'google backtracking'):"):
-    # Display user message in chat message container
-    st.chat_message("user").markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
+# User input form for generating a new question
+with st.form(key="input_form"):
+    company = st.text_input("Company", value="Google")  # Default value: Google
+    difficulty = st.selectbox("Difficulty", ["Easy", "Medium", "Hard"], index=1)  # Default: Medium
+    topic = st.text_input("Topic (e.g., Backtracking)", value="Backtracking")  # Default: Backtracking
+    
+    generate_button = st.form_submit_button(label="Generate")
 
-    # Find the top question based on user input
-    top_question = find_top_question(prompt)
+if generate_button:
+    # Clear session state and start a new conversation history with system prompt
+    st.session_state.messages = [{"role": "assistant", "content": system_prompt}]
+    
+    # Create a query from user inputs and find the most relevant question
+    query = f"{company} {difficulty} {topic}"
+    top_question = find_top_question(query)
     
     # Prepare a detailed prompt for GPT using the top question's details
     detailed_prompt = (
@@ -94,15 +95,19 @@ if prompt := st.chat_input("Enter a LeetCode-related query (e.g., 'google backtr
     # Generate response using GPT-4 with detailed prompt and debugging logs
     response = generate_response(detailed_prompt)
 
-    # Display assistant response in chat message container
+    # Display assistant response in chat message container and add to session history
     with st.chat_message("assistant"):
         st.markdown(response)
     
-    # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})
 
+# Display chat messages from history on app rerun (for subsequent conversation)
+for message in st.session_state.messages[1:]:  # Skip the system message
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
 st.sidebar.markdown("""
 ## About
 This is a Real-World Interview Question Generator powered by OpenAI's API.
-Enter a company name, topic and level of difficulty, and it will transform a relevant question into a real-world interview scenario!
+Enter a company name, topic, and level of difficulty, and it will transform a relevant question into a real-world interview scenario!
 """)